Navegando por Orientadores "SILVA, Cleison Daniel"

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Acesso aberto (Open Access)
Aplicação e comparação de técnicas de classificação automática de documentos: um estudo de caso com o dataset do domínio jurídico “Victor”
(Universidade Federal do Pará, 2024-02-01) MARTINS, Victor Simões; SILVA, Cleison Daniel; http://lattes.cnpq.br/1445401605385329; https://orcid.org/0000-0001-8280-2928
The application of Natural Language Processing (NLP) and Artificial Intelligence (AI) in the Brazilian legal context is a rapidly growing area that can alter the way legal professionals work, given the volume of generated text. Among the possible applications of NLP and AI is the automatic classification of documents, which, among other things, can be employed in the automation of the digitization process of Judicial Proceedings that are still in physical form. Therefore, this work applies and compares AI algorithms for the classification of legal documents. The algorithms are divided into two different approaches. The first approach (I) separates the computational representation process of the text from the classifier training itself and applies SVM and Logistic Regression in conjunction with computational representations based on TF-IDF, Word2Vec, FastText, and BERT. The second approach (II) simultaneously performs the computational representation of documents and the training of the classifier, applying Deep Learning algorithms based on recurrent neural networks, specifically ULMFiT (Universal Language Model Fine-tuning), and HAN (Hierarchical Attention Networks). The studied dataset is named VICTOR, composed of documents from the Supreme Federal Court (STF) of Brazil. The research concludes that both approaches can be applied to the classification of legal documents from the employed dataset. Additionally, despite being less computationally expensive, the classification pipelines of Approach I, which use the computational representation of the document with TF-IDF, yield results equivalent to pipelines employing Deep Learning. Furthermore, embedding documents specialization with data from the dataset under study, improves the performance of pipelines that employ Word2Vec, FastText and ULMFiT, compared to pipelines that apply the generic representations of these, i.e., models pre-trained with data from the general context.
Acesso aberto (Open Access)
Aprendizado em conjunto aplicado à classificação da imagética motora
(Universidade Federal do Pará, 2025-01-20) JORGE, Vitor da Silva; SILVA, Cleison Daniel; http://lattes.cnpq.br/1445401605385329; https://orcid.org/0000-0001-8280-2928
Acesso aberto (Open Access)
AutoBCI: interface cérebro-máquina com configuração hiperparamétrica automatizada
(Universidade Federal do Pará, 2021-03-11) VILAS BOAS, Vitor Mendes; TEIXEIRA, Otávio Noura; http://lattes.cnpq.br/5784356232477760; SILVA, Cleison Daniel; http://lattes.cnpq.br/1445401605385329; https://orcid.org/0000-0001-8280-2928
Motor Imagery-based Brain-Computer Interfaces (MI-BCI) allow control of devices without the use of peripheral nerves and muscles, based on voluntary modulation of brain electrophysiological activity. The challenge imposed on the typical non-invasive MI-BCI is to extract patterns that describe the motor intention in signals collected by electroencephalography (EEG) and classify them to generate reliable commands to the application. For that, the selection of suitable processing techniques as well as the correct parameterization of the system are fundamental in the adjustment of effective classification models. The configuration of multiple hyperparameters in the processing chain, commonly performed manually and unspecified by the user, tends to generate rigid models that are unable to generalize well in different individuals, especially due to the high variability of MI patterns observed among them. The use of strategies to estimate these hyperparameters according to the subject’s specificities is presented as a more effective approach and has been explored in recent studies. This work proposes a structure based on Bayesian learning incorporated into a new open source MI-BCI computational platform for automatic configuration of hyperparameters. The system integrates all the basic steps of the ICM-IM subband architecture, from the acquisition to the control of a virtual application. Various processing techniques make up a large configuration space to search for particular hyperparametric instances that maximize system performance and draw the user the manual adjustment task. Data from 72 subjects in three public EEG sets were used in offline and online simulations, whose goal was to validate the operation of the implemented modules and to investigate the effects of the automatic configuration on the classification performance and on the effective control of the application. A significant improvement in the accuracy of classification was observed when using automatic configuration based models of the system compared to models generated from frequent configurations in the literature. The results suggest that the optimization of hyperparameters produces more assertive models in the classification of IM patterns of different users and tends to contribute to a more effective control of the application. It is concluded that this study contributes to the design of ICM-IM more effective in recognizing the user’s particular IM patterns by providing a complete experimental environment, customizable and uncomplicated to use by automated configuration. The option for more efficient techniques in signal processing also proved to be viable and are also considered contributions of this work.
Acesso aberto (Open Access)
ICM Space Game: uma interface baseada na imaginação de movimentos
(Universidade Federal do Pará, 2023-03-10) CALVINHO, Jhoanyn Valois Fantin; MERLIN, Bruno; http://lattes.cnpq.br/7336467549495208; HTTPS://ORCID.ORG/0000-0001-7327-9960; SILVA, Cleison Daniel; http://lattes.cnpq.br/1445401605385329; https://orcid.org/0000-0001-8280-2928
Brain-Machine Interfaces can help users participate in routine tasks, such as moving around. The scientific community works daily in an attempt to offer increasingly robust Brain-Machine Interface systems, with better responses to user commands. However, these works usually focus on improving the system itself. Therefore, the objective of this work is to offer an alternative to the users to help in the learning of the use of equipment of a Brain-Machine Interface based on the imagination of movements. For this, a computational tool based on a virtual game is developed in an attempt to improve the accuracy of users in controlling the devices of these systems. The results show that the tool works when connected to a Brain-Machine Interface, and can serve as an alternative in the process of collecting EEG signals. Throughout this work, programming languages dedicated to ICMs, such as OpenVibe, are used, as well as a language widely used in the programming of electronic games, Python. In the experiment carried out with 8 volunteers, there is no discrepant difference between the classification rates performed with the aid of the conventional protocol and the ICM Space Game, approximately 56% for both
Acesso aberto (Open Access)
Redes neurais convolucionais aplicadas à inspeção de componentes do vagão ferroviário
(Universidade Federal do Pará, 2020-02-03) ROCHA, Rafael de Lima; GOMES, Ana Claudia da Silva; http://lattes.cnpq.br/9898138854277399; SILVA, Cleison Daniel; http://lattes.cnpq.br/1445401605385329; https://orcid.org/0000-0001-8280-2928
The railcar is one of the most important assets in a mining company, where tons of ore are transported daily by it, besides, the railcar can be used to transport people. Therefore, the inspection of defects in structural components of the railcar is a very important activity, making it possible to avoid problems in railway logistics, as well as to prevent accidents. The inspection task is performed visually by an operating technician who is exposed to accidents where the inspection is performed, in addition to the possibility of human error due to stress, fatigue, and others. The pad is a rail component analyzed in this work, where it is responsible for the primary suspension, a role that is important in the railcar dynamics. Thus, the purpose of this work is to use deep learning techniques, specifically convolutional neural networks (CNN) for the component inspection. CNN classifies the image of the structural component analyzed concerning the possible state it is in the railway, absent pad, undamaged pad, and damaged pad. Also, it intends to investigate the contribution of the component image in the frequency domain obtained through the magnitude and phase of the discrete Fourier transform (DFT) of the original image (spatial domain) in the CNN classification process. Histogram equalization and increasing the number of images through data augmentation techniques are also examined to evaluate their collaborations in improving classification performance. The results of CNN inspection of the pad prove to be quite inspiring, especially when the spatial component image is used together with the DFT magnitude image of the original image as CNN inputs, which are superior when only the original (spatial) image of the component is used, achieving a classification accuracy of 95.65%. In particular, the method that uses the increase in the number of training images by the data augmentation and the spatial domain and frequency (magnitude) images achieves the highest accuracy, with 97.47%, which represents approximately 385.5 correctly classified images from a total of 395.2 images.